Applied regression and time series analysis: Concepts methods and practical applications

Applied Regression and Time Series Analysis: Concepts, Methods, and Practical Applications is a comprehensive textbook designed for students, researchers, and professionals in statistics, data science, economics, business analytics, and engineering. The book is meticulously structured to bridge the gap between theoretical concepts and their real-world applications, ensuring that readers not only understand the mathematical foundations but can also confidently implement models using modern computational tools such as Excel, R, and Python.
This book presents a balanced combination of theory, step-by-step methodology, and applied case studies. It covers the full spectrum of regression analysis—from simple linear models to multiple regression, logistic regression, and nonlinear approaches—before guiding the reader into time series analysis and forecasting models, including ARIMA, SARIMA, VAR, ARCH/GARCH, and modern machine learning-based forecasting methods.
One of the standout features of this book is its "learn-by-doing" philosophy. Every chapter includes:

• Clear conceptual explanations
• Worked-out examples with step-by-step solutions
• Graphical illustrations and plots for better understanding
• Practical exercises using real datasets from finance, marketing, climate, healthcare, and manufacturing
• Hands-on coding sections in both R and Python to ensure practical competence

By the end of the book, the reader will have a complete toolkit for modeling relationships between variables, diagnosing and improving models, and forecasting future trends with accuracy.


Benefits of Studying This Book
Studying this book offers multiple advantages for learners at different stages of their academic and professional journey:
1. Academic Excellence

• Comprehensive coverage of regression and time series topics aligns with the syllabi of BCA, MCA, BTech, MTech, MBA, Statistics, and Data Science courses.
• Helps in preparing for competitive exams such as UGC NET, GATE (Statistics), and other professional certification tests.
• Includes illustrated explanations to make statistical concepts intuitive and engaging.

2. Practical and Industry-Relevant Skills

• Readers learn how to apply regression and time series models to solve real business, engineering, and research problems.
• Teaches data preparation, model selection, and diagnostics, ensuring robust and reliable analysis.
• Guides on forecasting for decision-making in finance, sales, supply chain, and policy planning.

3. Hands-On Computational Expertise

• Readers get step-by-step coding tutorials in Excel, R, and Python, enabling them to execute models with modern software.
• Encourages self-learning with datasets and case studies provided in the book and online resources.

4. Strong Problem-Solving Ability

• Each concept is reinforced through numerical examples, exercises, and projects.
• Builds the reader’s ability to critically evaluate models, check assumptions, and improve performance.

5. Career Advancement

• Enhances employability in data-driven roles such as Data Analyst, Business Analyst, Forecasting Specialist, Quantitative Researcher, and Financial Analyst.
• Supports academic research by equipping learners with tools to analyze data and present findings convincingly.

How to Study This Book
This book is designed to be flexible for different learning goals—whether you are preparing for an exam, doing a research project, or solving real business problems.